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Combining Image Enhancement Techniques and Deep Learning for Shallow Water Benthic Marine Litter Detection

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1752))

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Abstract

The scarcity of information about benthic marine litter especially in developing countries hampers the implementation of targeted actions to minimize the extent of its impacts. This study developed a system using image processing and deep learning methods for detecting/tracking marine macro litter that can efficiently identify and quantify its amount in benthic environments in shallow coastal areas. Shallow underwater litter detection poses several challenges. First is the low quality of images. Second is the difficulty in recognizing litter brought by their varying visual characteristics. Third is the lack of available data for training. Underwater images of litter were collected from marine litter hotspots in coastal areas in southern Philippines. This study experimented with various object detection algorithms. The best object detection model is then paired with various image enhancement techniques to determine the optimal combination. Among the combinations that were tested, YOLOv5n combined with CLAHE gave the best performance for simple binary task (litter or not litter) with a mAP@0.5 of 0.704. Furthermore, the results showed that applying underwater image enhancement techniques provides noticeable improvement for object detection models on detecting marine litter.

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Notes

  1. 1.

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Author information

Authors and Affiliations

  1. AIR-ES Lab, University of the Philippines Mindanao, Davao City, Philippines

    Gil Emmanuel Bancud, Alex John Labanon, Neil Angelo Abreo & Vladimer Kobayashi

Authors
  1. Gil Emmanuel Bancud
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  2. Alex John Labanon
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  3. Neil Angelo Abreo
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  4. Vladimer Kobayashi
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Corresponding author

Correspondence to Gil Emmanuel Bancud .

Editor information

Editors and Affiliations

  1. University of Sydney, Sydney, Australia

    Irena Koprinska

  2. University of Bari Aldo Moro, Bari, Italy

    Paolo Mignone

  3. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  4. Warsaw University of Technology, Warsaw, Poland

    Szymon Jaroszewicz

  5. Heidelberg University, Heidelberg, Germany

    Holger Fröning

  6. UniCredit, Rome, Italy

    Francesco Gullo

  7. University of Lisbon, Lisbon, Portugal

    Pedro M. Ferreira

  8. Roche, Basel, Switzerland

    Damian Roqueiro

  9. Barcelona Supercomputing Center, Barcelona, Spain

    Gaia Ceddia

  10. Halmstad University, Halmstad, Sweden

    Slawomir Nowaczyk

  11. University of Porto, Porto, Portugal

    João Gama

  12. University of Porto, Porto, Portugal

    Rita Ribeiro

  13. UPC BarcelonaTech, Barcelona, Spain

    Ricard Gavaldà

  14. University of Naples Federico II, Naples, Italy

    Elio Masciari

  15. University of North Carolina, Charlotte, USA

    Zbigniew Ras

  16. ICAR-CNR, Rende, Italy

    Ettore Ritacco

  17. University of Pisa, Pisa, Italy

    Francesca Naretto

  18. Aalen University of Applied Sciences, Aalen, Germany

    Andreas Theissler

  19. Warsaw University of Technology, Warszaw, Poland

    Przemyslaw Biecek

  20. KU Leuven, Leuven, Belgium

    Wouter Verbeke

  21. University of Duisburg-Essen, Essen, Germany

    Gregor Schiele

  22. Graz University of Technology, Graz, Austria

    Franz Pernkopf

  23. AMD, Dublin, Ireland

    Michaela Blott

  24. UniCredit, Rome, Italy

    Ilaria Bordino

  25. UniCredit, Milan, Italy

    Ivan Luciano Danesi

  26. National Agency for New Technologies, Rome, Italy

    Giovanni Ponti

  27. Unicredit, Rome, Italy

    Lorenzo Severini

  28. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  29. University of Bari Aldo Moro, Bari, Italy

    Giuseppina Andresini

  30. University of Lisbon, Lisbon, Portugal

    Ibéria Medeiros

  31. University of Lisbon, Lisbon, Portugal

    Guilherme Graça

  32. Northwestern University, Chicago, USA

    Lee Cooper

  33. Roche, Basel, Switzerland

    Naghmeh Ghazaleh

  34. University of Lausanne, Lausanne, Switzerland

    Jonas Richiardi

  35. Novartis, Basel, Switzerland

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  36. Novartis, Basel, Switzerland

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  37. Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy

    Arif Canakoglu

  38. Politecnico di Milano, Milan, Italy

    Sara Pido

  39. Politecnico di Milano, Milan, Italy

    Pietro Pinoli

  40. University of Waikato, Hamilton, New Zealand

    Albert Bifet

  41. Halmstad University, Halmstad, Sweden

    Sepideh Pashami

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Bancud, G.E., Labanon, A.J., Abreo, N.A., Kobayashi, V. (2023). Combining Image Enhancement Techniques and Deep Learning for Shallow Water Benthic Marine Litter Detection. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1752. Springer, Cham. https://doi.org/10.1007/978-3-031-23618-1_9

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  • DOI: https://doi.org/10.1007/978-3-031-23618-1_9

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-23618-1

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